Normally you breeze through PyTorch’s high‑level APIs, but the moment you have to write operators from scratch without a framework, your true colors show—formulas swirl in your head but get stuck when translated into code; you can talk through the logic but freeze up when you start coding.

There’s an open‑source practice platform on GitHub called TorchCode, specifically designed to fix this problem. It turns handwritten deep learning operators into a LeetCode‑style experience.

Built on a Jupyter environment, it curates 40 high‑frequency problems covering foundational operators, attention mechanisms, full architectures, and training optimization—the heavy‑hitters in interviews.

Each problem comes with an automatic judge: real‑time verification of outputs, gradient flow, and numerical stability, with clear color‑coded pass/fail feedback. You’ll know immediately where things went wrong.

Stuck? There are hints. Finished? There are reference solutions. Progress tracking is laid out clearly.

Deployment is extremely friendly: Docker one‑click startup, or directly use Hugging Face Spaces to start practicing online. Each problem also supports one‑click opening in Google Colab, saving you the hassle of local environment setup.

If you want to truly master the underlying mechanics of large models or prepare for AI position interviews, this set of problems is worth going through from start to finish.

GitHub: https://github.com/duoan/TorchCode

duoan/TorchCode

Source: https://github.com/duoan/TorchCode

title: TorchCode emoji: 🔥 colorFrom: red colorTo: yellow sdk: docker app_port: 7860 pinned: false

🔥 TorchCode

Crack the PyTorch interview. Practice implementing operators and architectures from scratch — the exact skills top ML teams test for.

An interactive coding platform, but for tensors. Self-hosted. Jupyter-based. Instant feedback.

PyTorch (https://pytorch.org) Jupyter (https://jupyter.org) Docker (https://www.docker.com) Python (https://python.org)

License: MIT

GitHub stars (https://github.com/duoan/TorchCode) · GitHub Container Registry (https://ghcr.io/duoan/torchcode) · Hugging Face Spaces (https://huggingface.co/spaces/duoan/TorchCode)

Problems · GPU · Star History Chart (https://star-history.com/#duoan/TorchCode&Date)

🎯 Why TorchCode?

Top companies (Meta, Google DeepMind, OpenAI, etc.) expect ML engineers to implement core operations from memory on a whiteboard. Reading papers isn’t enough — you need to write softmax, LayerNorm, MultiHeadAttention, and full Transformer blocks from scratch.

TorchCode gives you a structured practice environment with:

No cloud. No signup. No GPU needed. Just make run — or try it instantly on Hugging Face.

🚀 Quick Start

Option 0 — Try it online (zero install)

Launch on Hugging Face Spaces (https://huggingface.co/spaces/duoan/TorchCode) — opens a full JupyterLab environment in your browser. Nothing to install.

Or open any problem directly in Google Colab — every notebook has an Open In Colab (https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/01_relu.ipynb) badge.

Option 0b — Use the judge in Colab (pip)

In Google Colab, install the judge from PyPI so you can run check(...) without cloning the repo:

!pip install torch‑judge

Then in a notebook cell:

from torch_judge import check, status, hint, reset_progress
status()           # list all problems and your progress
check("relu")      # run tests for the "relu" task
hint("relu")       # show a hint

Option 1 — Pull the pre‑built image (fastest)

docker run -p 8888:8888 -e PORT=8888 ghcr.io/duoan/torchcode:latest

If the registry image is unavailable for your platform, use Option 2 instead. This is the common path on Apple Silicon / arm64.

Option 2 — Build locally

make run

make run will try the prebuilt image first and automatically fall back to a local build when needed.

Open http://localhost:8888 — that’s it. Works with both Docker and Podman (auto‑detected).

Option 3 — Standalone Web UI (Next.js + FastAPI)

For a modern, standalone coding experience with an integrated IDE and dual‑pane layout:

1. Start Backend (FastAPI):

   pip install -r api/requirements.txt
   python -m uvicorn api.main:app --port 8000 --reload
   

2. Start Frontend (Next.js):

   cd web
   npm install
   npm run dev
   

3. Open http://localhost:3000 in your browser.

📋 Problem Set

Frequency: 🔥 = very likely in interviews, ⭐ = commonly asked, 💡 = emerging / differentiator

🧱 Fundamentals — “Implement X from scratch”

The bread and butter of ML coding interviews. You’ll be asked to write these without torch.nn.

🧠 Attention Mechanisms — The heart of modern ML interviews

If you’re interviewing for any role touching LLMs or Transformers, expect at least one of these.

🏗️ Architecture & Adaptation — Put it all together

⚙️ Training & Optimization

🎯 Inference & Decoding

🔬 Advanced — Differentiators

⚙️ How It Works

Each problem has two notebooks:

Workflow

1. Open a blank notebook → Read the problem description
2. Implement your solution → Use only basic PyTorch ops
3. Debug freely → print(x.shape), check gradients, etc.
4. Run the judge cell → check("relu")
5. See instant colored feedback → ✅ pass / ❌ fail per test case
6. Stuck? Get a nudge → hint("relu")
7. Review the reference solution → 01_relu_solution.ipynb
8. Click 🔄 Reset in the toolbar → Blank slate — practice again!

In‑Notebook API

from torch_judge import check, hint, status

check("relu")                # Judge your implementation
hint("causal_attention")     # Get a hint without full spoiler
status()                     # Progress dashboard — solved / attempted / todo

📅 Suggested Study Plan

Total: ~12–16 hours spread across 3–4 weeks. Perfect for interview prep on a deadline.

🏛️ Architecture

┌──────────────────────────────────────────┐
│ Docker / Podman Container                │
│                                          │
│ JupyterLab (:8888)                       │
│ ├── templates/ (reset on each run)       │
│ ├── solutions/ (reference impl)          │
│ ├── torch_judge/ (auto‑grading)          │
│ ├── torchcode-labext (JLab plugin)       │
│ │     🔄 Reset — restore template        │
│ │     🔗 Colab — open in Colab           │
│ └── PyTorch (CPU), NumPy                 │
│                                          │
│ Judge checks:                            │
│ ✓ Output correctness (allclose)          │
│ ✓ Gradient flow (autograd)               │
│ ✓ Shape consistency                      │
│ ✓ Edge cases & numerical stability       │
└──────────────────────────────────────────┘

Single container. Single port. No database. No frontend framework. No GPU.

🛠️ Commands

make run    # Build & start (http://localhost:8888)
make stop   # Stop the container
make clean  # Stop + remove volumes + reset all progress

🧩 Adding Your Own Problems

TorchCode uses auto‑discovery — just drop a new file in torch_judge/tasks/:

TASK = {
    "id": "my_task",
    "title": "My Custom Problem",
    "difficulty": "medium",
    "function_name": "my_function",
    "hint": "Think about broadcasting...",
    "tests": [ ... ],
}

No registration needed. The judge picks it up automatically.

📦 Publishing torch‑judge to PyPI (maintainers)

The judge is published as a separate package so Colab/users can pip install torch‑judge without cloning the repo.

Automatic (GitHub Action)

Pushing to master after changing the package version triggers .github/workflows/pypi-publish.yml, which builds and uploads to PyPI. No git tag is required.

1. Bump version in torch_judge/_version.py (e.g. __version__ = "0.1.1").
2. Configure PyPI Trusted Publisher (one‑time):
   • PyPI → Your project torch‑judge → Publishing → Add a new pending publisher
   • Owner: duoan, Repository: TorchCode, Workflow: pypi-publish.yml, Environment: (leave empty)
   • Run the workflow once (push a version bump to master or Actions → Publish torch‑judge to PyPI → Run workflow); PyPI will then link the publisher.
3. Release: commit the version bump and git push origin master.

Alternatively, use an API token: add repository secret PYPI_API_TOKEN (value = pypi‑... from PyPI) and set TWINE_USERNAME=__token__ and TWINE_PASSWORD from that secret in the workflow if you prefer not to use Trusted Publishing.

Manual

pip install build twine
python -m build
twine upload dist/*

Version is in torch_judge/_version.py; bump it before each release.

❓ FAQ

Do I need a GPU?
No. Everything runs on CPU. The problems test correctness and understanding, not throughput.

Can I keep my solutions between runs?
Blank templates reset on every make run so you practice from scratch. Save your work under a different filename if you want to keep it. You can also click the 🔄 Reset button in the notebook toolbar at any time to restore the blank template without restarting.

Can I use Google Colab instead?
Yes! Every notebook has an “Open in Colab” badge at the top. Click it to open the problem directly in Google Colab — no Docker or local setup needed. You can also use the Colab toolbar button inside JupyterLab.

How are solutions graded?
The judge runs your function against multiple test cases using torch.allclose for numerical correctness, verifies gradients flow properly via autograd, and checks edge cases specific to each operation.

Who is this for?
Anyone preparing for ML/AI engineering interviews at top tech companies, or anyone who wants to deeply understand how PyTorch operations work under the hood.

🤝 Contributors

Thanks to everyone who has contributed to TorchCode.

duoan · Ando233 · abhijitmjj · HareshKarnan · ThierryHJ

Auto‑generated from the GitHub contributors graph with avatars and GitHub usernames.

Built for engineers who want to deeply understand what they build.
If this helped your interview prep, consider giving it a ⭐

☕ Buy Me a Coffee

Scan to support